From Power BI to Microsoft Fabric
The platform engineering transition, in the order that actually works
Power BI · Microsoft Fabric · SQL · Python · DAX
The transition this course is about#
Report development and platform engineering are different jobs. This course covers the second one, assuming you are already good at the first.
Prerequisites#
Comfortable with Power BI semantic models and DAX. Willing to write SQL. A Fabric trial capacity is enough to follow every exercise.
The through-line#
Every module is organised around a decision rather than a feature, because the difficulty in Fabric is not operating the product — it is knowing which of its choices you are making permanently.
Curriculum
5 sections · 15 lessons
Foundations you cannot skip
- SQL beyond SELECT
- Query plans and why they matter
- Dimensional modelling review
Fabric architecture
- Lakehouse or warehouse: the decision that constrains everything
- Medallion layering with rules per boundary
- Where transformation logic belongs
Building the platform
- Incremental ingestion from day one
- Silver: typing, deduplication, business keys
- Gold: shaping for the semantic model
Semantic models on Fabric
- Direct Lake and its silent fallback
- Calculation groups
- Performance measurement
Operating it
- Deployment pipelines
- Capacity and throttling
- Documentation that gets read
Free material on this topic
Microsoft Fabric Analytics Platform
An end-to-end Fabric implementation — lakehouse layering, incremental ingestion, dataflows, semantic model and deployment pipelines — with the decisions that are hard to reverse called out.
Power BI Inventory Control Tower
An inventory dashboard built around the four decisions a stock controller makes each week, with a semantic model that stays fast on 40 million rows.
Power BI Developer to Fabric Engineer: A Roadmap
A staged plan for moving from report development into platform engineering on Microsoft Fabric — including an honest read on which existing skills carry over.